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Environment International

journal homepage: www.elsevier.com/locate/envint

Health benefits and control costs of tightening particulate matter emissionsstandards for coal power plants - The case of Northeast Brazil

Daniel B. Howarda, Jesse Théb, Rafael Soriac, Neal Fannd, Roberto Schaeffere,Jean-Daniel M. Saphoresf,⁎

a Civil and Environmental Engineering, University of California, Irvine, CA 92697, USAbMechanical and Mechatronics Engineering, University of Waterloo, ON N2L 3G1, Canadac Departmento de Ingeniería Mecánica, Escuela Politécnica Nacional, Ladrón de Guevara E11·253, Quito, Pichincha EC 17-01-2759, EcuadordNational Expert and Team Lead for Assessing the Benefits of Air Quality, Office of Air Quality Planning and Standards, U.S. Environmental Protection Agency, Durham,NC 27709, USAe Energy Planning Program, COPPE, Universidade Federal do Rio de Janeiro, 21941-972 Rio de Janeiro, RJ, Brazilf Civil and Environmental Engineering, Economics, University of California, Irvine 92697, USA

A R T I C L E I N F O

Handling Editor: Yong-Guan Zhu

Keywords:Health impactsPollution control costsParticulate matterEmission standardsCoal power plantsElectricity grid dispatchAir qualityEnergy policy

A B S T R A C T

Exposure to ambient particulate matter (PM) caused an estimated 4.2 million deaths worldwide in 2015.However, PM emission standards for power plants vary widely. To explore if the current levels of these standardsare sufficiently stringent in a simple cost-benefit framework, we compared the health benefits (avoided mon-etized health costs) with the control costs of tightening PM emission standards for coal-fired power plants inNortheast (NE) Brazil, where ambient PM concentrations are below World Health Organization (WHO) guide-lines. We considered three Brazilian PM10 (PMx refers to PM with a diameter under x micrometers) emissionstandards and a stricter U.S. EPA standard for recent power plants. Our integrated methodology simulates hourlyelectricity grid dispatch from utility-scale power plants, disperses the resulting PM2.5, and estimates selectedhuman health impacts from PM2.5 exposure using the latest integrated exposure-response model. Since theemissions inventories required to model secondary PM are not available in our study area, we modeled onlyprimary PM so our benefit estimates are conservative. We found that tightening existing PM10 emission stan-dards yields health benefits that are over 60 times greater than emissions control costs in all the scenarios weconsidered. The monetary value of avoided hospital admissions alone is at least four times as large as thecorresponding control costs. These results provide strong arguments for considering tightening PM emissionstandards for coal-fired power plants worldwide, including in regions that meet WHO guidelines and in de-veloping countries.

1. Introduction

The combustion of fossil fuels is increasingly of concern, not onlybecause of greenhouse gas emissions, but also because of the adversehealth impacts of the air pollutants it produces. For coal, these pollu-tants include sulfur dioxide, nitrogen oxides, carbon monoxide, heavymetals such as mercury, and particulate matter (Smith et al., 2013). Wefocus here on particulate matter (PM), a complex mixture of tiny solidparticles and liquid droplets emitted from both natural and anthro-pogenic sources, because of all the common air pollutants it is thelargest contributor to global mortality and morbidity. PM, and espe-cially PM2.5 (PMx denotes particulate matter with a diameter under x

micrometers), contains inhalable particles small enough to penetratedeep in the respiratory system, resulting in respiratory (e.g., asthmaaggravation) and cardiovascular morbidity, and mortality from cardi-ovascular, lung cancer, and respiratory diseases (WHO, 2013). As aresult, ambient PM2.5 is now the fifth largest contributor to globaldisease (Brauer et al., 2015). The impact of PM pollution has beenworsening: between 1990 and 2015, the number of deaths attributableto ambient PM2.5 exposure increased from 3.5 million to 4.2 million(Cohen et al., 2017). However, PM emissions standards for coal-firedpower plants - a large source of anthropogenic PM (National ResearchCouncil, 2010) - vary widely, depending on plant size, age, technology,and jurisdiction (Zhang, 2016).

https://doi.org/10.1016/j.envint.2019.01.029Received 13 September 2018; Received in revised form 18 December 2018; Accepted 10 January 2019

⁎ Corresponding author.E-mail addresses: dbhoward@uci.edu (D.B. Howard), jesse.the@weblakes.com (J. Thé), rafael.soria01@epn.edu.ec (R. Soria), Fann.Neal@epa.gov (N. Fann),

roberto@ppe.ufrj.br (R. Schaeffer), saphores@uci.edu (J.-D.M. Saphores).

Environment International 124 (2019) 420–430

Available online 22 January 20190160-4120/ © 2019 Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/BY-NC-ND/4.0/).

T

A number of studies have quantified air quality and/or health im-pacts from fossil fuel power plants under various scenarios and controlstrategies at the regional (Levy et al., 1999, 2002a-2002b; Levy andSpengler, 2002; Zhou et al., 2003; Frost et al., 2006; Carbonell et al.,2007; Hao et al., 2007; Carreras-Sospedra et al., 2010; Vutukuru et al.,2011; McDonald-Buller et al., 2016) and national (Zhou et al., 2006;Markandya and Wilkinson, 2007; Levy et al., 2009; Machol and Rizk,2013; Mokhtar et al., 2014; Driscoll et al., 2015) levels. A few studieshave modeled hourly electricity grid unit commitment and dispatch tobetter capture the link between electricity production and ambient airconcentrations (Kerl et al., 2015; Buonocore et al., 2016a-2016b;Rodgers, 2016); however, these studies used linear approximations tomodel the relationship between pollutant exposure and health impacts,which tend to underestimate health impacts at low concentrations andoverestimate health impacts at high concentrations, compared to non-linear exposure-response models (Apte et al., 2015). We did not findany studies that combined hourly electricity grid unit commitment anddispatch with atmospheric dispersion and non-linear exposure-responsemodels.

Several studies have compared the health benefits with the controlcosts of reducing various types of power plant emissions. Some papersfocused on the acid rain program in the U.S. After analyzing this pro-gram, Burtraw et al. (1998) reported that its health benefits outweighits expected costs with a high level of confidence. In a subsequent in-vestigation, Chestnut and Mills (2005) confirmed that the health andenvironmental benefits of the U.S. acid rain program (~US$100 billionannually) dwarf its costs (~US$3 billion annually). Burtraw et al.(2001) analyzed instead the cost-effectiveness of reducing NOx emis-sions from electric power plants; they concluded that the benefits of theU.S. EPA mandated state implementation plans would more likely ex-ceed costs with an annual cap than with a seasonal cap. More recently,Buonocore et al. (2016a) found that the net health benefits of the U.S.Clean Power Plan, which decreases emissions of various air pollutants(including PM) as a result of implementing a CO2 standard, are in theorder of US$12 billion annually.

In China, Li et al. (2004) obtained a benefit-cost ratio ranging from1 to 5 for reducing PM10 emissions from coal-fired power plants in theheavily polluted Shanghai area. In a more recent study of severalheavily polluted Chinese cities, Zhang et al. (2015) reported that thehealth benefits of reducing coal power plant emissions are 29 to 32times greater than the corresponding control costs. Also of interest isthe multi-continent cost-benefit analysis of local air pollution andglobal climate change by Bollen et al. (2009), who estimated that thebenefit-cost ratio of controlling power plant emissions ranges from 2 to32.

While these studies cover a broad array of contexts, to our knowl-edge no published study has yet combined hourly electricity grid unitcommitment and dispatch simulations with atmospheric dispersion anda non-linear exposure-response model. In this paper, we adopt a con-ceptually simple cost-benefit framework to explore if different PM10

emission standards are sufficiently stringent (i.e., if incremental bene-fits from tightening these standards are “close” to the correspondingcontrol costs). We apply this framework to compare the health benefits(avoided health costs) and the control costs of tightening PM10 emissionstandards for coal power plants in Northeast (NE) Brazil, where am-bient PM concentrations are below World Health Organization guide-lines (WHO, 2018).

Apart from our methodology, which is well-suited for areas that donot have the emissions inventories required by photochemical modelsto simulate secondary PM formation,1 our paper makes several

contributions. First, we provide new (conservative) benefit estimates oftightening PM standards based on the monetarily value of health ben-efits, which we compare with emissions control costs. Second, our casestudy adds to the thin literature dealing with the health impacts ofthermal power plants in Latin America, which currently consists of twostudies in Brazil (Alves and Uturbey, 2010; Avelino et al., 2015), one inMexico (Lopez et al., 2005), and another one for three large LatinAmerican cities (Bell et al., 2006). Third, while many previous studieshave focused on areas with high PM concentrations (Zhou et al., 2003,2006; Li et al., 2004; Bell et al., 2006; Hao et al., 2007; Guttikunda andJawahar, 2014; Zhang et al., 2015), our case study is in a region whereannual average ambient PM2.5 concentrations are below the WHO airquality guideline of 10 μg/m3 (WHO, 2018). Fossil fuel power plants insuch areas are probably less likely to be upgraded to curb emissions ofair pollutants and the local population may erroneously feel safe fromadverse health impacts caused by air pollution.

2. Methods and data

2.1. Study area

Our study area is in the northeast of Brazil (NE Brazil), a countrythat relies on renewable energy (mainly hydroelectricity) for over 80%of its electricity production (REN21, 2017).

NE Brazil is comprised of nine states that span over 1.5million km2

and have a combined population of 54 million people (InstitutoBrasileiro de Geografia e Estatística, n.d.); see Fig. 1. The main cities ofNE Brazil, Recife, Fortaleza and Salvador, have populations of 3.69,3.61, and 3.57 million respectively (Instituto Brasileiro de Geografia eEstatística, n.d.).

NE Brazil, a semi-arid region, has some of the world's highest ca-pacity factors for wind and solar electricity production, along with alarge hydroelectric potential (Krauter, 2005; da Silva et al., 2016).Renewable electricity (including hydroelectricity) in Brazil is plannedto expand from 103.1 GW of installed capacity in 2013 to 164.1 GW in2023, while non-renewable electricity is forecasted to expand from 21.3GW to 31.7 GW over the same time period (a total capacity increase of57.3% over 10 years).

The NE grid is one of five large grids in Brazil's national inter-connected power system (Brazil also has an isolated system in theAmazon Region). Its total installed capacity exceeds 29,000MW, and itsannual electricity demand tops 85,000 GWh (ONS, n.d.). Table 1 showsthe installed capacity of electric generators regulated by the centraldispatch authority Operador Nacional do Sistema Elétrico (ONS) in NEBrazil.

NE Brazil has only two large coal power plants: Porto do Pecém I(two 360MWunits) and Porto do Pecém II (one 360MW unit). Both arelocated within 30 km of the Fortaleza metropolitan area (Fig. 1), alongwith several diesel and gas power plants. The location of these plantsmotivated our selection of the Fortaleza area for this study.

A review of regulatory documents indicates that Brazil has at leastthree different PM10 emission standards (but currently no PM2.5 stan-dard) for coal power plants: 28.15 g/kWh (Ministério de Minas eEnergia, 2007); 390mg/Nm3 (0.69 g/kWh; National Counsel of theEnvironment (CONAMA)) (Ferreira, 2015); and 204mg/Nm3 (0.36 g/kWh; for power plants funded by Brazil's National Development Bank(BNDES)) (Governo do Estado do Ceará, n.d.). We were unable to de-termine why the PM10 emission standard in the 2030 National EnergyPlan is so much higher than the other emission standards. TheCONAMA and BNDES standards were converted to g/kWh using datafrom the Porto do Pecém power plants. By comparison, the U.S. EPAPM10 emission standard for electric utility steam generating units over73MW built after 05/03/11 is 0.09 lb./MWh (0.04 g/kWh or 22.6mg/Nm3) (U.S. EPA, 2015), which is among the most stringent standards inthe world (Zhang, 2016).

This sets the stage for our study. We analyzed the health benefits

1 PM can either be emitted directly into the air (primary PM, which has bothman-made and natural sources) or be formed in the atmosphere (secondary PM)from gaseous precursors such as SO2, NOx, ammonia, and nonmethane volatileorganic compounds (WHO, 2013).

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and control costs of the following three scenarios:

• Scenario 1: the PM10 emission standard is cut from 28.15 g/kWh to0.69 g/kWh;

• Scenario 2: the PM10 emission standard is reduced from 0.69 g/kWhto 0.36 g/kWh; and

• Scenario 3: the PM10 emission standard is tightened from 0.36 g/kWh to 0.04 g/kWh.

2.2. Integrated assessment methodology

Fig. 2 present an overview of our integrated methodology. First, wemodeled hourly unit commitment and dispatch of the NE Brazil elec-tricity grid level for an entire year (2015).

Second, we dispersed the resulting hourly primary PM2.5 emissions

(after converting PM10 emissions using the PM2.5/PM10 ratio as ex-plained in Subsection 2.2.2) for year 2015 using the dispersion modelCALPUFF View™. We did not consider secondary PM because the nat-ural and anthropogenic emissions inventories required to simulatesecondary PM formation are not available in our study area, whichmakes our results conservative.

Third, the resulting PM2.5 concentrations were processed inBenMAP (U.S. EPA, 2017a) using the latest IER model (Cohen et al.,2017) to capture the non-linear relationship between pollutant ex-posure and health impacts. The monetized health benefits were thencompared with the emission control costs, which we calculated using anintegrated environmental control model (Carnegie Mellon University,2017).

2.2.1. Electricity system dispatch model (PLEXOS®)To model hourly power plant dispatch for a whole year (2015), we

built a spatially and temporally resolved electricity dispatch model forthe electric grid of Northeast (NE) Brazil using the PLEXOS® model.Recent studies have used PLEXOS® to assess large-scale renewable in-tegration (Palchak et al., 2017), capacity expansion planning (Shirleyand Kammen, 2015), and pumped storage hydropower (Koritarov et al.,2014).

Electricity dispatch models simulate optimal power plant dispatchto match load (i.e. electricity demand) and generation (i.e. electricitysupply), while accounting for the marginal cost of electricity generationand technical, economic, and environmental constraints. We selectedPLEXOS® because it has a broad range of functionalities, an intuitiveuser interface, and an extensive documentation. Alternatives toPLEXOS®, such as the NEWAVE model developed by Brazil's PowerSector Research Center (CEPEL), are proprietary and therefore un-available.

Fig. 1. Study area.

Table 1Northeast Brazil electricity system capacity (2015).

Type Number ofgenerators

Total capacity(MW)

Percentage of systemcapacity

RenewableWind 291 7650 25.9Solar 52 1325 4.5Hydro 11 14,721 49.8

ThermalBiomass 1 17 0.1Natural gas 7 2094 7.1Coal 2 1080 3.7Oil 29 2684 9.1

Total 393 29,570 100.0

Source: Operador Nacional do Sistema Eletrico (ONS, n.d.).

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Designing an electricity dispatch model that optimizes unit com-mitment requires defining load, a transmission network, generators,fuels, emissions, storages, reserves, and various constraints. As loadprofiles are not public in Brazil, we also had to develop an hourly loadprofile for NE Brazil for 2015 by projecting to 2015 the 2013 NE Brazilload profile obtained from Brazil's National System Operator (ONS,Operador Nacional de Sistema Elétrico). We relied on the load fore-casting algorithm in PLEXOS®, and assumed that electricity demandgrows by 4.77% per year (Torrini et al., 2016).

Transmission system data were obtained from Miranda et al. (2017),who recently studied the integration of large-scale wind power into theNE Brazil electricity grid. Each generator (node) was connected to atransmission line, and each transmission line was constrained by itsmaximum flow capacity.

We modeled all of the 393 generators in NE Brazil that are centrallydispatched by Brazil's ONS (Table 1). For each generator, a set ofproperties was defined, as shown in Fig. 3. Most of the data used fordeveloping our NE Brazil PLEXOS® model were collected by our part-ners at the Center for Energy and Environmental Economics (CENER-GIA) at the Universidade Federal do Rio de Janeiro. These data weresourced primarily from Brazil's national electricity operation authorityONS and from the national electricity regulator ANEEL (Agência Na-cional de Energia Elétrica). Additional data were obtained directly fromONS and ANEEL.

Hydroelectric power plants, which represent 49.8% of NE Brazil'selectric system capacity, were dispatched to minimize the cost ofthermal generation over our period of analysis. Each hydroelectric plantwas constrained by setting the initial fill level of each reservoir to thepercentage of NE hydro storage capacity that each reservoir con-tributes, multiplied by the stored energy in January 2015. The finallevel of each reservoir was set to the percentage of NE hydro storagecapacity that each reservoir contributes, multiplied by the stored en-ergy in December 2015. Moreover, the maximum energy available permonth for each hydroelectric plant was based on prior year hydro-electric dispatch records to account for seasonality in precipitationpatterns and electricity demand (Energy Exemplar, n.d.).

Following previous studies (e.g., Blair et al., 2014), the hourlygeneration of wind and solar power plants across NE Brazil was simu-lated based on location specific wind speeds and solar irradiation pro-files. We relied on a CENERGIA wind generation model and the UnitedStates National Renewable Energy Laboratory System Advisory Model(Blair et al., 2014).

Properties specific to each thermal generator include maximumcapacity, heat rate, minimum stable level, ramp up and ramp down

rates, minimum uptime and downtime, start time and emissions rates.2

PM10 emission rates were based on PM10 emission standards in theMinistry of Mines and Energy 2030 Energy Plan (2007): 0.042 g/kWhfor natural gas generators, 0.37 g/kWh for diesel generators and 0.49 g/kWh for biomass generators. For coal power plants, we used the PM10

emission standards discussed above (28.15 g/kWh, 0.69 g/kWh, 0.36 g/kWh, and 0.04 g/kWh). We simulated the NE Brazil electric grid dis-patch and emissions under each of the four PM10 emission standardsconsidered, holding all other properties of the NE Brazil electricitysystem constant between simulations. Hourly emissions were calculatedfor each power plant based on actual dispatch, assuming that allthermal power plants abide by the relevant emission standard for thescenario considered.

After simulating NE Brazil's electricity grid dispatch for year 2015,spatially resolved emission profiles containing the time-varying hourlyemissions for each generator (8760 entries per generator) were enteredinto CALPUFF View™.

2.2.2. Atmospheric dispersion of PM emissions (CALPUFF)We selected CALPUFF for simulating the dispersion of primary PM

emissions because it is widely used, extensively validated, re-commended by the U.S. EPA (U.S. EPA, 2017b), and well suited for thisstudy. CALPUFF is a non-steady-state Lagrangian Gaussian puff modelthat accounts for complex terrain effects, overwater transport, long-range transport, coastal interactions, building downwash, wet and dryremoval, and simple chemical transformations. CALPUFF View™ (thecommercial version of CALPUFF we used; Lakes Environmental, n.d.)includes built-in modules for processing input data (meteorologicaldata, geophysical terrain) and results from the dispersion of selectedpollutants.

Dispersion modeling for our study required spatially resolved powerplant emissions, meteorological, and topographic data. NE Brazil fits ina 1200 km by 1500 km rectangle. We selected a 320 km×320 km areawith a 5.4 km×5.4 km grid around Fortaleza to capture air qualitychanges caused by the PM emissions of thermal generators in that area,

Generators

Fuels

Emissions

Storage

Reserves

Nodes

Transmission

Regions

Scenarios

Load

Spa�al and temporal emissions

Air quality concentra�on

grid

Baseline AQ

Control AQ

Popula�on

Incidence

C-R func�ons

Valua�on equa�on

Monetary valua�on of health impacts

Plexos CALPUFF BenMAP

Control inputs

Meteorology

Surface

Baseline concentra�ons

Emissions

Coastal boundary

Terrain

Flux boundary

Fig. 2. Methodology overview.

2 Maximum capacity is each generator's installed capacity. The heat rate is theamount of fuel required to produce one unit of electricity (MJ/kWh). Theminimum stable level is the minimum stable generation. Ramp up/down rateslimit the rate at which generation can increase/decrease (MW/min). Theminimum up/down time is the minimum number of hours a generator must beleft on or off for in any commitment cycle. The start time is the amount of timeeach generator requires to reach its minimum stable level. Emission rates arethe rate of production of air pollutant emissions (g/kWh).

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which include the only two large coal power plants in NE Brazil. Out ofthe 39 operational thermal power plants in NE Brazil in 2015 (Table 1),eight are within our study area.

Hourly meteorological conditions for our study area were developedas WRF (weather research and forecasting) files with a 4 km resolutionby the University of Waterloo, Canada. The WRF files include surfacemeteorological data, such as wind speed and direction, temperature,surface pressure, precipitation and cloud cover, as well as verticalprofiles of wind speed and direction, temperature, and pressure.

We processed the WRF meteorological data in CALMET (a module ofCALPUFF View™) to develop wind and temperature fields on a three-dimensional grid with a 4 km resolution. To process geophysical terraininputs for CALMET, we used three publicly available map files (SRTM1,SRTM3 and GTOPO30) to account for land and ocean effects, becauseFortaleza and the Porto do Pecém power plants are close to the Atlanticcoast.

The PM10 emissions simulated in PLEXOS® were converted to PM2.5

using the ambient ratio method (Boldo et al., 2006) before dispersion inCALPUFF View™. For this conversion, we calculated a ratio by mass ofprimary PM2.5/PM10 of 0.775 by averaging all reported PM2.5 to PM10

emissions (by mass) for coal, oil, and natural gas generation in theUnited States (U.S. EPA, 2018a) as suitable Brazilian data were notavailable. As the ratio PM2.5/PM10 varies by fuel source, it wasweighted for each fuel source based on their contribution to emissionsin the NE Brazil electric grid.

2.2.3. Health outcomes modeling (BenMAP)To assess how changes in long-term human exposure to PM2.5

concentrations affect selected health outcomes, we used the U.S. EPA'sEnvironmental Benefits Mapping and Analysis Program (BenMAP) (U.S.EPA, 2017a). We are aware of a dozen of multinational air pollutionhealth impact assessment tools but we chose BenMAP for its versatility.BenMAP performs spatial and temporal calculations based on differ-ences in air pollution concentrations between a baseline and a control

scenario. It then combines concentration changes with spatially re-solved population and baseline disease incidence data and uses con-centration-response (C-R) functions to estimate the number of peopleaffected (and the cost) for various health outcomes.

Population data with a 10 km×10 km resolution for 2015 wereextracted from the BenMAP regional datasets (U.S. EPA, 2017c) sourcedfrom the United Nations Socioeconomic Data and Applications Center(SEDAC). We did not use Brazilian census data because the finestavailable resolution (municipality level) is coarser than the SEDACdata. Baseline disease incidence data and ambient PM2.5 concentrationswere also downloaded from the BenMAP regional datasets. Both po-pulation and disease incidence data were discretized into 16 age groupsusing the 2015 Brazilian age pyramid (a graph showing the distributionof a population [on the horizontal axis] by age [on the vertical axis] andgender, with men on the left and women on the right) to match therequirements of the C-R functions we used.

We collected ambient PM2.5 concentrations, which ranged from 5 to10 μg/m3, from the 2013 Global Burden of Disease (Brauer et al., 2015).To combine the 5.4 km×5.4 km PM2.5 air quality concentrations (si-mulated in CALPUFF View™) from electric grid emissions with the10 km×10 km PM2.5 ambient concentrations, we interpolated ambientPM2.5 concentrations using inverse-distance weighting (see Fig. 4 PanelD) because it has been shown to be preferable to Kriging or Thiessenpolygons for environmental data (Akkala et al., 2010). We assumed thatambient concentrations reflect PM2.5 emissions under the 0.36 g/kWhstandard because BNDES provided funding to the Porto do Pecém coalpower plants to meet this standard (Governo do Estado do Ceará, n.d.).Therefore, the PM2.5 power plant emissions under the 0.36 g/kWhstandard were subtracted from the ambient concentrations in all sce-narios before adding the simulated power plant emissions.

To estimate premature mortality, we relied on the 2017 global IERmodel (Cohen et al., 2017). The IER model is the only model that spansthe entire global range of PM2.5 exposure for the five main causes ofPM-related mortality, which is necessary to capture the effects of high

FUELS

Objects• Gas• Coal• Biomass• Heavy fuel oil

• Light fuel oil• Liq. natural

gas

Memberships• Generators• Emissions

Proper�es• Price• Shadow price• Emissions

produc�on rate

EMISSIONS

Objects• PM10

Memberships• Generators• Fuels

Proper�es• Price• Max

produc�on• Produc�on

rate• Removal rate

SIMULATION

Execute• Model• Scenario

Simula�on• Horizon• Long-term

plan• Medium-term

& short-term schedules

Se�ngs• Transmission• Produc�on• Stochas�c• Solver• Performance• Diagnos�cs

RESERVES

Objects• Spinning

reserve • Regula�on up• Regula�on

down

Memberships• Generators• Regions

Proper�es• Min. provision• Max. response• Timeframe• Value of

reserve shortage

STORAGE

Objects• Head storage• Tail Storage

Memberships• Generators

Proper�es• Max. volume• Ini�al volume• Natural inflow• Max. Spill

TRANSMISSION

Objects• Regions• Nodes• Lines

Memberships• Generators• Nodes• Line to/from• Regions

Proper�es• Units• Allow dump energy• Allow unserved

energy• La�tude/Longitude

GENERATORS

Objects• Hydro• Combined cycle gas

turbine (CCGT)• CCGT-CHP• Internal combus�on

engine• Gas turbine• Steam-coal• Steam-biomass• Wind• Photovoltaic

Memberships• Fuels • Nodes• Emissions • Reserves

Proper�es• Units• Max. capacity• Min. stable level• Heat rate• Variable O&M• Start cost

• Start �me• Shutdown cost• Ra�ng• Min. up/down �me• Ramp up/down rate• Ramp charge• Units out• Maintenance rate• Forced outage• Time to repair• Max energy• Emissions removal

rate

Fig. 3. Electric grid simulation data.

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A 28.15 g/kWh PM10 standard B 0.69 g/kWh PM10 standard

C 0.36 g/kWh PM10 standard D 0.04 g/kWh PM10 standard

E Ambient PM 2.5 concentrations

Fig. 4. Fortaleza study area average annual PM2.5 concentrations (μg/m3).

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PM2.5 air quality concentrations in Scenario 1. In a study comparing theIER model with seven other prediction methods previously used inburden assessments, the IER model was found to be the best predictor ofrelative risk changes due to PM2.5 exposure (Burnett et al., 2014).Table 2 presents the C-R functions used to estimate mortality andhospital admissions.

A shown in Table 2, we relied on epidemiological studies from theU.S. and Europe, because for many health endpoints Latin Americanresults are not available for PM2.5 (Bell et al., 2006). To assess theimpact of using a different C-R function, we also quantified prematuremortality using a PM2.5C-R function developed by Lepeule et al. (2012)for low PM2.5 concentrations in the US, which matched the range ofambient PM2.5 concentrations in NE Brazil (see the sensitivity analysissection below).

We derived valuation functions for each type of hospital admissionconsidered by updating the standard BenMAP cost of illness valuationfunctions for hospital admissions with Brazil hospital costs. Brazilianhospital admissions costs were sourced from a World Bank study (LaForgia and Couttolenc, 2008) and updated to 2015 using the Brazilianmedical wage index (World Bank, n.d.). All of our health benefits andcontrol costs are expressed in 2015 US$.

To calculate the value of a statistical life (VSL) for Brazil for 2015,we followed the U.S. EPA (2018b) recommended approach:

⎜ ⎟= ∗ ⎛⎝

⎞⎠

∗

Brazil US BrazilUS

BrazilVSL VSL GDPPCGDPPC

PPP ,2015 20152015

2015

0.8

2015

(1)

where GDPPC denotes the gross domestic product per capita (obtainedby dividing the gross domestic product, adjusted for purchasing parity,by the total population); and Brazil PPP2015 is the purchasing powerparity conversion factor for Brazil, i.e., the number of units of Brazil'scurrency required to buy the same amounts of goods and services inBrazil as $1 in the U.S.

The U.S. EPA (2018b) recommends a U.S. VSL for 2015 of $10million. For 2015, the Brazil and U.S. GDPPC were respectively $15,615and $56,207 in PPP-adjusted dollars, and the PPP GDP conversionfactor for Brazil was 1.9 (World Bank, n.d.). The 0.8 exponent comesfrom the OECD (2012), and represents the elasticity between incomeand willingness to pay to avoid adverse health effects. The resulting2015 VSL for Brazil is US$6,819,641.

2.2.4. Emission control costsTo compare the avoided health costs (health benefits here) from

tightening the PM10 emission standard with the corresponding controlcosts for the two Porto de Pecém coal power plants in our study area,we analyzed control costs using the Integrated Environmental ControlModel (IECM) (Carnegie Mellon University, 2017). Our analysis relied

on plant specific data, including plant capacity, plant life, and coal type.We considered the two post-combustion particulate controls for coalpower plants available in the IECM: cold-side electrostatic precipitators(ESP) and fabric filters (FF), whose PM2.5 collection efficiency is 98.0%and 99.7% respectively (Zhang, 2016).

Capital costs include particle collectors, ductwork, fly ash equip-ment, fans, general facilities costs, engineering fees, pre-productioncosts, process contingency costs, and interests. Per net MW, capitalcosts total US$47,170 for an ESP and US$56,780 for a FF. Fixed costsinclude operating and maintenance labor, maintenance material andadministrative labor. Per MWh, fixed costs sum to US$0.3293 for anESP and US$0.4166 for a FF. Variable costs include electricity, waterand waste disposal, for a total per MWh of US$0.4790 for an ESP andUS$0.4986 for a FF.

The equipment and material costs were assumed to be 38.4%greater for Brazil than for the U.S., based on an estimate of the addi-tional costs (freight and insurance costs, import fees, customs expensesand Brazilian taxes) of importing equipment (Köberle et al., 2018).Brazilian labor costs were assumed to be 21% of U.S. labor costs, basedon the Brazil to U.S. gross national income per capita ratio.

We calculated that the annualized cost of removing 1 tonne (metricton) of PM10 is US$87.98 with an ESP and US$119.90 with a FF for a360MW unit. To estimate the control costs of reducing PM10 emissionsScenarios 1 and 2, we assumed that an ESP would be used because thisapproach is cheaper and sufficiently efficient (98.0% PM2.5 removalefficiency). Since Scenario 3 requires that 99.0% of PM10 emissions beremoved, we assumed a FF would be used because it has a 99.7% PM2.5

removal efficiency.

3. Results

3.1. Electric grid emissions

Results from our PLEXOS® simulations for 2015 show an annualsystem load of 96,385 GWh and an annual electricity generation of97,593 GWh. Total simulated generation was 1.3% greater than loadand the simulated hydroelectric generation was within 3.2% of actualhydroelectric generation. When generation is greater than load and noenergy storage resources are available, electricity generation must becurtailed or dissipated into heat (dump energy). The dump energy inour simulations was 1218 GWh (1.3% of load) and the unserved loadwas only 10 GWh (0.01% of load) (10 GWh=96,385+11,218 GWh –97,593 GWh).

Table 3 displays total PM10 emissions by standard from the entireNE Brazil electric grid for 2015. It shows that emissions from coalpower plants in our study area could be reduced by 240,617 t of PM10/year (=248,679-8062, or 96.8%) under Scenario 1, by 2892 t of PM10/

Table 2Selected C-R functions for mortality & hospital admissions.

Health endpoint Health impact function authors PM2.5 range (μg/m3) Location Age range (years)

MortalityCerebrovascular disease Cohen et al. (2017) 1–300 Global 25–99Chronic obstructive pulmonary disease Cohen et al. (2017) 1–300 Global 30–99Ischemic heart disease Cohen et al. (2017) 1–300 Global 25–99Lung cancer Cohen et al. (2017) 1–300 Global 30–99Lower respiratory infection Cohen et al. (2017) 1–300 Global 30–99All causes (infants) Loomis et al. (1999) 4–85 Mexico City 0–1

Hospital admissionsAll cardiovascular Moolgavkar (2000) 4–86 Los Angeles, CA 18–64All cardiovascular Zanobetti et al. (2009) 6.1–24 26 U.S. Communities 65–99Asthma Babin et al. (2007) Not reported Washington D.C. 0–17Asthma Slaughter et al. (2003) 5–60 Seattle, WA 18–64Chronic lung disease (excl. asthma) Moolgavkar (2000) 4–86 Los Angeles, CA 18–64All respiratory Zanobetti et al. (2009) 6.1–24 26 U.S. Communities 65–99

Note: “C-R" stands for “concentration-response”.

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year (=8062-5170, or 35.9%) under Scenario 2, and by an additional2803 t of PM10/year (5170-2367, or 54.2%) under Scenario 3.

3.2. Resulting ambient air quality changes

Table 4 summarizes different mean PM2.5 concentrations frompower plant emissions. The maximum annual mean PM2.5 concentra-tions are 91.7, 2.26, 1.18, and 0.35 μg/m3 under the 28.15, 1.18, 0.36,and 0.04 g/kWh standards respectively. We note that under the 0.69 g/kWh, 0.36 g/kWh and 0.04 g/kWh standards, PM2.5 concentrations inNE Brazil are below the World Health Organization (WHO) annualmean guideline of 10 μg/m3 PM2.5 and 24-hour mean guideline of25 μg/m3 (WHO, 2018) even after accounting for ambient PM2.5 con-centrations. These values illustrate the drastic reduction in PM2.5 underthe 3 scenarios considered.

Panels A to D of Fig. 4 display average annual PM2.5 concentrationsunder each standard considered to illustrate how they change as PM10

standards are being tightened. As expected, the highest average annualconcentrations are closest to the coal power plants and disperse wes-terly, in accordance with the annual wind rose shown in Fig. 5. Panel Eof Fig. 4 displays ambient PM2.5 concentrations for reference (describedin Section 2.2.3). As explained above, the Porto de Pecém coal powerplants are assumed to abide by the 0.36 g/kWh PM10 standard.

PM2.5 concentration results from CALPUFF View™ only account forpower plant emissions in the region surrounding Fortaleza. The (gridcell-level) highest annual mean PM2.5 concentration is substantially lessthan the observed ambient PM2.5 concentrations under the 0.36 g/kWhand 0.04 g/kWh standards, which is expected because electricity gridemissions contribute only a portion of total ambient PM2.5. Ambientconcentrations were incorporated in the health outcomes modeling toassess the impact of air quality improvements on human health.

3.3. Human health impacts

Table 5 (and the abstract art figure) summarizes the expected healthbenefits and the control costs of tightening the PM10 standard for coalpower plants under each of the three scenarios considered. These re-sults are based on average annual PM2.5 concentrations. Implementing

Scenario 1 would prevent ~168 premature deaths and reduce hospitaladmissions by 16,257 cases per year, which is worth US$1.264 billionper year with our assumptions. Cutting the PM10 emission standardfrom 0.69 to 0.36 g/kWh (Scenario 2) would prevent an additional 3.7premature deaths and 198 hospital admissions, which represents an-nual savings of US$26.4 million. Further tightening the PM10 emissionstandard from 0.36 to 0.04 g/kWh would avoid 4.2 premature deathsand 194 hospital admissions, for annual savings of US$30.0 million.

Although premature deaths drive the value of health losses underthe three scenarios considered, the number of additional hospital ad-missions is 46 to 97 times greater than the number of premature deathsas most people affected by PM2.5 pollution will suffer from respiratoryailments or cardiovascular disease.

3.4. Comparison of health benefits with emissions control costs

Our calculations show that tightening the PM10 emission standardunder Scenario 1 would cost US$21.1 million per year and cut240,617 t of PM10. Going from 0.69 to 0.36 g/kWh (Scenario 2) wouldcost US$0.25 million per year and decrease annual PM10 emissions by2892 t. Implementing Scenario 3 from would cost US$0.34 million peryear and remove 2803 t of PM10.

Although these amounts are substantial, they are dwarfed by theexpected health benefits of reducing PM pollution. Indeed, the value ofavoided health costs is respectively 60 times, 103 times, and 89 timesgreater than the cost of controlling emissions under Scenario 1, 2, and3. These results reflect that the relative risk of disease incidence per unitincrease in pollution is greater at low exposure than at high exposure(health impacts depend non-linearly on PM exposure concentrations)(Burnett et al., 2014). The costs of hospital admissions related to PMpollution are by themselves at least four times larger than the emission

Table 3Annual PM10 emissions by standard (metric tons).

PM10 Standard

28.15 g/kWh 0.69 g/kWh 0.36 g/kWh 0.04 g/kWh

Emission sourceCoal (tonne of PM10/year)

246,663 6046 3154 351

Oil (tonne of PM10/year)

1284 1284 1284 1284

Natural gas (tonne ofPM10/year)

732 732 732 732

Total (tonnes of PM10/year)

248,679 8062 5170 2367

Table 4Air quality concentrations after dispersion (PM2.5 μg/m3).

PM10 standard Average annual mean of all grid cells (PM2.5 μg/m3)

Maximum annual mean of all grid cells (PM2.5 μg/m3)

Maximum 24-hour mean of all grid cells (PM2.5 μg/m3)

28.15 g/kWh 2.01 [0.00, 91.7] 91.7 2600.69 g/kWh 0.05 [0.00, 2.26] 2.26 6.380.36 g/kWh 0.03 [0.00, 1.18] 1.18 3.330.04 g/kWh 0.01 [0.00, 0.35] 0.35 2.17

Notes. 1) The average annual mean concentration is the average annual PM2.5 concentration across all grid cells in our study area. The minimum and maximumvalues are presented in brackets. 2) The maximum annual mean concentration refers to the grid cell with the highest annual mean concentration. 3) The maximum24-hour mean concentration represents the grid cell with the highest 24-hour concentration.

Fig. 5. Year 2015 wind rose for the city of Fortaleza.

D.B. Howard et al. Environment International 124 (2019) 420–430

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control costs.

3.5. Sensitivity analysis

Uncertainty was analyzed at each modeling stage. As hydroelectricplants represent 49.8% of NE Brazil's electricity infrastructure capacity(ONS, n.d.), we analyzed the impact that dry year conditions have onemissions by restricting reservoir levels and maximum energy dispatchto 2012 levels, which was the driest year in the NE Brazil water basinsince 1983 (CHRS, 2016). As expected, total annual PM10 emissionsincreased most for the more stringent standards (e.g., by 18.5% underthe 0.36 g/kWh PM10 standard), and little under the 28.15 g/kWhstandard.

During the air quality modeling stage, we compared concentrationsobtained by converting PM10 to PM2.5 before and after dispersion,which differed by an average of ~1% over the study area.

During our health impact analyses, we compared adult prematuremortality cases from the IER model with the Lepeule et al. (2012) C-Rfunction. The latter was developed for low PM2.5 concentrations(8–18 μg/m3) and has a higher relative risk estimate between PM2.5

exposure and mortality. We found that adult mortality cases were 2.9 to4.9 times higher with the Lepeule C-R function, which further high-lights that our results are conservative.

4. Conclusions

In this paper, we combined an hourly electric grid simulation modelfor year 2015 (built using PLEXOS®), with spatially resolved atmo-spheric dispersion (via CALPUFF View™), and human health impactsestimation for various health outcomes related to PM2.5 exposure (viaBenMAP) to quantify the health benefits of tightening PM10 standardsand compare them to PM emissions control costs. We found that thevalue of avoided health costs is 60 times greater than the emissioncontrol costs when cutting the PM10 emission standard from 28.15 g/kWh to 0.69 g/kWh (Scenario 1), 103 times greater when reducing itfurther from 0.69 g/kWh to 0.36 g/kWh (Scenario 2), and 89 timesgreater when tightening it from 0.36 g/kWh to 0.04 g/kWh, which isamong the most stringent standards in the world (Zhang, 2016).

These results are noteworthy because they show that even in an areawhere ambient PM2.5 ambient concentrations are below the WHOguidelines, the local population would experience substantial healthgains from tightening PM10 emission standards.

Although a key driver of our results compared to some of the olderstudies (e.g., Li et al., 2004; Lopez et al., 2005; Pervin et al., 2008;Bollen et al., 2009) is the higher value of a statistical life (VSL), de-creasing the VSL would not qualitatively change our conclusions that,from the point of view of society, stricter PM10 standards for coal powerplants should be implemented. Indeed, the mere benefit of avoidingPM-related illnesses leading to hospital admissions would exceedemission control costs by at least a factor four under each of the sce-narios we considered.

Furthermore, it is important to note that the range of expectedhealth benefits we report is conservative for several reasons. First, wedid not account for secondary PM, although its impact could be quitesubstantial (Behera and Sharma, 2010), because modeling secondaryPM requires complete natural and anthropogenic emissions inventories,which are not available for our study area (Sogabe, 2018). Second, wewere not able to consider mortality induced by PM exposure in peopleaged 1 to 25 years because of epidemiological data limitations. Third,we note that lenient environmental regulations attract poorer qualityinvestors (Dowell et al., 2000) and high-polluting joint ventures (Deanet al., 2005), which further degrade regional environmental quality andhuman health. Accounting for these secondary effects would further tiltthe balance in favor of more stringent PM emission standards.

Tightening standards is not sufficient, however. Standards need tobe enforced, which can be challenging, especially in developingTa

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389

D.B. Howard et al. Environment International 124 (2019) 420–430

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countries where plant-level monitoring data are rarely available(Barton et al., 2000).

In the future, it would be of interest to model secondary PM (whenthe necessary data become available) and to apply our methodology toareas with different geographic, demographic and electricity infra-structure conditions. Another avenue would be to explore the impact ofeven higher spatial resolution on results with appropriate terrain, me-teorological, and population data, and to investigate seasonal variationsin PM emissions and health impacts. Moreover, other pollutants couldbe modeled jointly with PM to analyze the co-benefits and costs of jointstandards. Finally, it would be of interest to study the labor impacts oftightening PM10 standards in NE Brazil, and more generally of in-creasing the share of renewable electricity.

Coal power plants will likely play an important role until theemergence of more cost-effective storage systems that can provide re-liable base loads from renewable energy plants. However, the sub-stantial difference between health benefits and control costs presentedin this study argues strongly for tightening and enforcing the emissionstandards of coal power plants to improve local and regional air quality,not only in heavily polluted areas but also in relatively clean areas(where PM2.5 concentrations are below WHO guidelines), including indeveloping countries.

Acknowledgements

The first author is grateful to the National Science Foundation for aGraduate Research Fellowship under Grant No. DGE-1321846. Anyopinion, findings, and conclusions or recommendations expressed inthis material are those of the authors(s) and do not necessarily reflectthe views of the National Science Foundation. Our co-authors in Braziland Ecuador supplied the Brazilian electric system data and indis-pensable modeling advice. Our collaboration was funded by the NSFGraduate Research Opportunities Worldwide program and the UnitedStates Agency for International Development. Professor Jesse Thé pro-vided the meteorological data and essential air quality modeling advice.Neal Fann supplied BenMAP data and gave invaluable advice through acollaboration funded by the NSF Graduate Research InternshipProgram. Energy Exemplar Pty Ltd. provided an academic license forthe Plexos integrated energy model and Lakes Environmental Softwareprovided software (including WRPLOT View) for air dispersion mod-eling; we are very grateful for these contributions.

The authors declare no competing financial interest.

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